{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = [[60], [140], [210], [380], [860]]\n",
    "y = [[42], [91], [96], [240], [420]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97636589465605261"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X2 = [[60], [140], [210], [380], [860]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y2 = model.predict(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  48.74026549]\n",
      " [  86.98018682]\n",
      " [ 120.44011799]\n",
      " [ 201.69995084]\n",
      " [ 431.13947886]]\n"
     ]
    }
   ],
   "source": [
    "print(y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ElSp6t7OIRCZ9sr0CLVmyhD59+lBYWMgf//hHXnnlFWrX/sk3p4mIRAxda6sC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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128624a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generateplt():\n",
    "    plt.figure()\n",
    "    plt.title('aa')#,fontproperties=font)\n",
    "    plt.xlabel('bb')#,fontproperties=font)\n",
    "    plt.ylabel('cc')#,fontproperties=font)\n",
    "    plt.axis([0, 1000, 0, 500])\n",
    "    plt.grid(True)\n",
    "    return plt\n",
    "\n",
    "plt = generateplt()\n",
    "plt.plot(X2, y2, 'k-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
